Method and system for predicting adherence to a treatment

ABSTRACT

Data characterizing an individual is received. Thereafter, one or more variables are extracted from the data so that, using a predictive model populated with the extracted variables, a likelihood of the individual adhering to a treatment regimen can be determined. The predictive model is trained on historical treatment regimen adherence data empirically derived from a plurality of subjects. Subsequently, data characterizing the determined likelihood of adherence can be promoted. Related apparatus, systems, techniques and articles are also described.

CROSS REFERENCE TO RELATED APPLICATION

The present application claims priority under 35 U.S.C. §119 to U.S.Provisional Application Ser. No. 61/151,152, filed Feb. 9, 2009,entitled “Method and System For Predicting Adherence to a Treatment” thedisclosure of which is incorporated herein by reference.

TECHNICAL FIELD

The subject matter described herein relates to techniques for predictingmedication adherence.

BACKGROUND INFORMATION

There is a risk associated with patients and others not adhering totreatment plans or treatment regimens suggested or ordered by a medicalprofessional. First of all, there is a health risk to the patient. Inaddition, the cost of providing medical care can become higher if apatient fails to comply or adhere to the suggested treatment plan orregimen. For example, a person may take arrhythmia medication for aheart condition. The medication may have some unpleasant side effects.So a younger person, may decide to forgo medication to avoid theunpleasant side effects. The risk associated with not taking themedication is much worse than the side effects. For example, theeffectiveness of the heart may drop to a point where the treatment planhas to be altered to prevent heart failure. There are countless otherexamples associated with medications. Other treatment regimens also needto be adhered to. For example, a young athlete recovering from a kneeinjury, such as a torn anterior cruciate ligament, may be put on arehabilitation program requiring painful visits to a physical therapistor painful weight training. If the plan is not adhered to, the recoverytime will be slowed and the recovery may not be complete. Thus, thepatient risks reinjuring the knee, which can be much more costly andmore painful for the patient. Furthermore, reinjuring the knee willresult in increased cost to insurance carriers. Thus, there is a needfor a system and method for predicting adherence to medical treatmentsand treatment regimens.

SUMMARY

In one aspect, data characterizing an individual is received.Thereafter, one or more variables are extracted from the data so that alikelihood of the individual adhering to a treatment regimen can bedetermined using a predictive model populated with the extractedvariables. The predictive model is trained on historical treatmentregimen adherence data empirically derived from a plurality of subjects.Thereafter, data characterizing the determined likelihood of adherencecan be promoted.

A treatment score indicative of the likelihood of adherence to thetreatment regimen can be generated and such treatment score can bepromoted (e.g., displayed in a GUI, persisted, transmitted to a remoteserver, etc.). The treatment score can be associated with one of aplurality of messages, the messages correlating to disjoint ranges ofthe treatment score. Thereafter, transmission of the associated messagecan be initiated to the individual. A delivery channel can be determinedfor the associated message and transmission of the message can be senton such delivery channel.

The treatment score can be associated with one of a plurality of sets ofmessages. The messages can correlate to disjoint ranges of the treatmentscore with each set of messages providing sequential guidance to theindividual to increase a likelihood of the individual adhering to themedical treatment. Each set of messages can further have an associatingtimeline for delivery so messages in the set can be individuallytransmitted messages based on the timeline for delivery.

Additional data characterizing the individual can be received such thatat least a portion of the additional data is generated subsequent toinitiation by the individual of the treatment regimen. Thereafter, oneor more variables can be extracted from the additional data with atleast one of the variables being affected by initiation of the treatmentregimen. A predictive model populated with the extracted variables fromthe additional data can be used to determine a likelihood of theindividual continuing to adhere to the treatment regimen. Suchdetermined likelihood of the continued adherence can be promoted.

A likelihood of patient response to the treatment regimen can bedetermined. Such a likelihood can be determined by extracting one ormore patient response variables from the data, determining, using asecond predictive model populated with the extracted patient responsevariables, a likelihood of the individual responding to the treatmentregimen, the predictive model being trained on historical treatmentregimen response data empirically derived from a plurality of subjects,and promoting data characterizing the determined likelihood of theindividual responding to the treatment regimen. A patient response scoreindicative of the likelihood of the individual responding to thetreatment regimen can be determined and promoted. The treatment scoreand the patient response score can be associated with one of a pluralityof sets of messages. Each set of messages can provide sequentialguidance to the individual to increase a likelihood of the individualadhering and responding to the medical treatment. Each set of messagescan have an associated timeline for delivery and messages in the set canbe transmitted based on the timeline for delivery.

In an interrelated aspect, data characterizing an individual can bereceived. Such data can be received at a plurality of sequential stageswhile the individual is undergoing a treatment regimen. At each stage,variables can be extracted from such data so that it can be determined,using a predictive model populated with the extracted variables, alikelihood of the individual adhering to a treatment regimen. Thepredictive model can be trained on historical treatment regimenadherence data empirically derived from a plurality of subjects. At eachstage, data characterizing the determined likelihood of adherence can bepromoted.

In still a further interrelated aspect, data characterizing anindividual can be received. Variables from the data can be extracted sothat it can be determined, using a predictive model populated with theextracted variables, a likelihood of the individual responding to atreatment regimen. The predictive model can be trained on historicaltreatment regimen responsiveness data empirically derived from aplurality of subjects. Data characterizing the determined likelihood ofresponsiveness can be promoted.

Articles are also described that comprise a machine-readable storagemedium tangibly embodying instructions that when performed by one ormore machines result in operations described herein. Similarly, computersystems are also described that may include a processor and a memorycoupled to the processor. The memory may temporarily or permanentlystore one or more programs that cause the processor to perform one ormore of the operations described herein.

The details of one or more variations of the subject matter describedherein are set forth in the accompanying drawings and the descriptionbelow. Other features and advantages of the subject matter describedherein will be apparent from the description and drawings, and from theclaims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram of a computer system.

FIG. 2 is a block diagram of an example modeling component.

FIG. 3 is a schematic view of another computer system.

FIG. 4 is a flow diagram of a computerized method for predictingadherence to a medical treatment.

FIGS. 5A and 5B is a flow diagram of another computerized methodspecifically tailored to pharmaceuticals and is associated with thecomputer system.

FIG. 6 is a graph of percentage of patients that adhere to variousprescription regimens over time, according to an example variation.

FIG. 7 is a graph showing that the days of adherence to a regimen infirst year was increased by 120 days in one case study.

FIGS. 8A and 8B is another example of a computerized method which isspecifically tailored to pharmaceuticals.

FIG. 9 is a process flow diagram illustrating a method for determining alikelihood of adherence to a medical treatment regimen.

DETAILED DESCRIPTION

FIG. 1 is a block diagram of a system 100 that is used to determine orpredict a person's potential adherence to a treatment plan, according toan example variation. The system 100 includes at least one predictivemodel component 200 for producing a model of the person's adherence to atreatment plan over time. The system 100 and the model component 200 caninclude one or more processors. The predictive model component 200 mayinclude one or more processors. In some instances, the predictive modelcomponent 200 may include a portion of a processor.

FIG. 2 is a block diagram of an example modeling component 200 used asmay be used in one or more variations of system 100. The modelingcomponent 200 includes a learning component 220 and a predictivecomponent 230. The learning component 220 processes historical data 210.The learning component determines and recognizes various patterns fromthe historical data 210. In this particular system, the historical data210 is related to other patients or persons and their adherence to atreatment plan. These treatment plans can be any type of treatment plan.For the sake of an example, the adherence discussed in this documentwill relate to prescription medications. The historical data 210 relatedto people and following treatment regimens is searched for patterns.Generally, the search of the historical data yields variables indicativeof the probability of adherence to a treatment plan or treatmentregimen. In this case, the search of the historical data yieldsvariables indicative of the probability of adherence to following aregimen of prescription medications. Once variables are found, thevariables are tested to make sure the historical data can be correlatedto the probability of patients and people adhering to their treatmentplan, such as following a regimen of prescription medications. In someinstances, one variable may be found. In other instances, a plurality ofvariables may be found. One or more variables may be combined to yield abetter correlation than that of a single variable. Generally, the bestcorrelation between the variable and variables and following thetreatment plan, such as following a regimen of prescription medications,is found. It should be noted, that the best correlation may include lessvariables than other models. Generally, a balance between the number ofvariables selected and the correlation to a treatment plan isdetermined. It should be noted, that for different treatment plans,different variables may correlate to the treatment plan.

Once the variable or variables are selected, a model is formed. Themodel can then be used as part of the predictive component 230. Themodel can be used with present data 240 to predict the likelihood ofcompliance with a medical treatment regimen or treatment plan. Theprobability of a particular person can be used to generate an output232, such as a score, from the predictive component 230. In someimplementations, reason codes for the score can also be generated forthe particular person or patient as another output 232. The score andthe reason codes are possible outputs 232 from the predictive component230 of the modeling component.

FIG. 3 is a schematic view of another computer system 300. The computersystem 300 includes a processor 310, a storage component 312 for holdinghistorical data regarding adherence by persons to a medical treatment,and a modeling component 320 that determines at least one variable fromthe historical data in the storage component. The at least one variablecorrelates the adherence to medical treatment to the historical data.The computer system also includes a prediction component 330 whichdetermines a likelihood of adherence to a treatment regimen. Themodeling component 320 uses the processor 310 and historical data whichis stored, at least temporarily, in the storage component 312. Theprediction component 330 uses current and past data on the patient, andthe processor to make a prediction of future behavior. The predictioncomponent 330 generates a treatment score 331 indicative of thelikelihood of adherence to the treatment regimen. The computer system300 may also include an action component 340 that generates a set ofactions that includes at least one of a plurality of messages sent tothe person in response to the value of the treatment score 331 generatedfor the person. The action component 340 generates a set of actions inresponse to a type of treatment. The type of treatment can be stored inthe storage component 312. In still other variations, the computersystem 300 further includes a patient response generator 350 forgenerating likelihood for patient response 351. The output of thelikelihood of adherence to a treatment regimen, such as the treatmentscore 331, and the likelihood for patient response 351 may both be inputto the action component 340 to determine a plurality of actionsincluding messages sent to a patient. The computer system 300 caninclude a reason component 360 for generating at least one reason code361 accompanying the treatment score. Of course, the reason component360 may interact with the processor 310, the storage unit 312, theprediction component 330, the action component 340, and the patientresponse generator 350 to produce reasons for the various outputs fromthese components. The various components, such as the modeling component320, the prediction component 330, the action component 340, and thepatient response generator 350, may include computer hardware orcomputer software or both. The various components also may include apart of the processor 310. The processor 310 can be one processor or aplurality of processors. In addition, the various components may includeindividual processors that may be dedicated to a single component. Allthe processors, in some variations, work together to form at least aportion of the computer system 300. All of the processors also canaccess the storage component 312 to obtain the necessary informationneeded to accomplish particular tasks. The components may also includestorage dedicated to the particular component.

The generated scores 331, 341, 351, 361 can then be used to producefollow ups of various types which are designed to keep the patientcomplying with the course of treatment. The model produced or generatedby the modeling component 320 that produces medication adherence scores,can also be combined with other models. For example, as discussed above,the medication adherence score model can be combined with an actionbased model. This allows certain constrained resources to be optimizedbased on key objectives. For example, the use of a cell center or theuse of an incentive budget for adherence can be maximized by a keyobjective of the overall health of the patients. The effort can beimproved so that the effort invested will generate the most impact.

The computer systems 100, 300 can be used to perform a computerizedmethod. FIG. 4 is one example variation of a computerized method 400 forpredicting adherence to a medical treatment. The computerized method 400for predicting adherence to a medical treatment includes studyinghistorical data regarding adherence by persons to a medical treatment410, determining at least one variable from the historical data thatcorrelates the adherence to medical treatment to the historical data412, developing a model that includes the at least one variable 414, andapplying the model to a persons data predict the likelihood of adherenceto a treatment regimen 416. The computerized method further includesgenerating a treatment score indicative of the likelihood of adherenceto the treatment regimen 418. In one variation, the historical data ispublicly available data. The computerized method 400 may also includegenerating a set of actions 420 that includes at least one of aplurality of messages sent to the person in response to the value of thetreatment score generated for the person. The set of actions of thecomputerized method 400 may also a determination of a channel fordelivery of the at least one message. In some implementations, the setof actions includes selection of the at least one message from aplurality of messages. In still another variation, the set of actionsincludes a frequency for delivery of a plurality of messages. In stillanother variation, the at least one variable varies in response to atype of medical treatment. In still another variation, the computerizedmethod 400 also includes determining likelihood for a patient response422. The likelihood of adherence to a treatment regimen and thelikelihood of the patient response can be used together, in somevariations, to determine actions 420, for example. The actions can bemessages sent to a patient. In other variations, the computerized method400 includes producing at least one reason code 424 to accompanying thetreatment code.

The action based models may also be used to market various treatmentplans. In one variation, the actions are used in marketingpharmaceutical products. Developing a model for a pharmaceutical companyrequires that the prescription data used must not reveal the patient. Insuch applications, it is necessary to go through additional steps toassure that the patients historical data used to form the model can notbe used to determine the identity of the patients. FIG. 5 is an examplevariation of another method 500 which is specifically tailored topharmaceuticals. The method 500 includes model development. Modeldevelopment includes specifying a set of criteria for patient selectionto be used for model development 510 and sending the criteria to datapartner 512. The data partner selects Rx records and returns only thepatient IDs to the model developer. The model developer appendsdemonstration data to encrypted patient IDs 514 to assure patientanonymity and to prevent re-identification of individuals through thisappended demographic information. The model developer returns encryptedpatient IDs matched to demographic information back to data vendor 516.Data vendor matches what is returned to them back to their database,strips off encrypted patient ID, and transmits de-identified Rx claimrecords to the model builder 518. In some variations, the transmissionis made in secure mode. The de-identified data to build the model formulti-attribute prescription scores 520.

Scoring and delivering the scores also requires additional steps to keepthe data secure. The pharmaceutical company identifies record(s) to bescored 530. The records may come from existing patient base or marketinginitiatives. Client creates and attaches a unique identifier to eachrecord 532, and then transmits records to the modeling and scoringcompany in a secure manner for batch scoring 534. Any requiredcommercially available information can be appended to records 536. Themodel is then executed to score each record 538. A new record is createdthat contains only the score and the unique identifier (score record)540, and this is transmitted (score plus unique identifier) to thepharmaceutical company in a secure manner 542. Of course, the aboveapplications assume that there are a plurality of records. It should beunderstood that the same process can be applied for use with one record.

FIGS. 8A and 8B is another example variation of a computerized method800 for masking the identity of patients from data about them. In theUnited States this is for meeting a governmental requirement that thereis less than a 25% chance of identifying a patient from a data set aboutpatients. The method includes joint creation of a data extractionspecification 810. The creation of the data extraction is a jointdevelopment between a data owner and a model developer. The data ownercreates data set of historical claims history based on agreed uponcriteria 812. In one example, historical claims history based on 500,000individuals. The data owner will remove duplicate patients which may beincluded in multiple files so that no duplicated names will appear inthe patient list created. The model developer prepares a dataset ofrandom names of individuals 814 in a selected geography. The dataset ofrandom names typically will include more than the number of names on thedata (historical claims history) the data company provides. In oneexample, the claims history is related to 500,000 patients and therandom name data set includes 2 million individuals. The model developerrandomly duplicates a subset of the names in order to remove thepossibility of identifying the patient names from the historical patientdata provided by the data company by finding duplicated names 816. Themodel developer then sends the resulting data to the data owner 818. Themodel developer destroys their copy of the data set of random names andaddresses 820 sent to the data owner. The data owner then creates a“super set” file that combines identifying information from the claimsdata set and the 2 million random names provided by the model developer822. The data owner then generates a random unique Match Key and appendsto each record of the “Super Set” 824. The model developer appends thirdparty data to the “super set” 826. The “super set” is stripped ofidentifying information leaving only the unique Match Key and theappended data to form blind data 828. Unless authorized by the modeldevelopers third party data providers, the model developer will encryptdata fields from all third party data providers except for the MatchKey. The data owner then uses its Match Key to append the historicalclaims history based on agreed upon criteria referenced in step 810, andstrips away all identifying information from the file 830. The dataowner destroys all data that does not match their data with historicalclaims history data. The data owner also destroys all third party data832.

Producing a model combats the problem of a drop in adherence to aregimen of prescription medicine. FIG. 6 shows a graph 600 of percentageof those that adhere to various prescription regimens over time. Thepercentage of patients that adhere to the regimen is shown on the y-axis610. The time is in units of months, which is shown on the x-axis 620.The graph 600 shows curves 630, 632, 634, 636, 638, 640, 642 forcompliance to prescriptions to treat multiple sclerosis, highcholesterol, hypertension, osteoporosis, asthma, respectively. Thecurves show that most people follow a particular regimen forprescription medication for about two months followed by a significantdrop in the percentage of those following the regimen. The number ofpatients (represented by percentage) who continue to follow the regimenfor prescribed drugs then, for the most part, is in a steady state or astate of fairly low decline.

One application of the method and apparatus described above is directedat identifying those individuals that are less likely to follow theregimen of prescribed drugs for a particular ailment. A model isgenerated. In some variations, extra measures are taken to assure thatthe historical data used to generate the model can not be used toidentify the patients associated with the historical data. Generatingthe model provides various insights about the patients. Some are knownand others are unknown. For example, the model or models capturepredictors regarding patient compliance that are consistent withindustry knowledge such as age, gender, and geographic location. Themodel or models also capture additional predictors of adherence that arenot well known, such as purchase behavior, income, and credit riskassociated with geographic region. Of course, there may be other notwell known predictors that may surface in other models. The known andthe lesser known predictors can be useful in tailoring messages toparticular patients.

The result of modeling is a score indicative of an observable metricclosely related to adherence to a regimen. This allows thepharmaceutical company to have an accurate and objective measure ofpatient value. Applying the results of modeling and predicting adherenceincludes sending messages and other information to selected patientsthat have been prescribed a medication. The pharmaceutical company canthen match the right investment (marketing materials, incentives, etc.)to the right patient to achieve improved health for the patient. Thepharmaceutical company will also have improved financial outcomes interms of sales of a particular prescribed drug. The end result, in someinstances is that the days of therapy (i.e. adherence to the regimen)was increased substantially in the first year. In one example, shown inFIG. 7, the days of adherence to a regimen in first year was increasedby 120 days. The patient's blood pressure was reduced, and product usageincreased $240. In other words, the patient was physically healthier andthe company selling the product was financially healthier. The insurancecompany had less cost than if patient did not adhere to the regimen andplaced him or herself into an unhealthy state.

A score provides means for managing customers more effectively andconsistently across channels, and enables greater precision insegmentation and agility in treatment actions. The score distillscomplex data into a single metric for operational efficiency, andprovides proxy and common lexicon for describing levels of patientadherence across the enterprise. The score also accelerates evaluationof patient value and adherence risk through automation.

It should be noted that the above example pertains to a regimen ofprescription medications. This is but one application of the method andapparatus. It should be noted that the method and apparatus are equallyapplicable to other types of regimens, such as a regimen for therapy.For example, if a patient undergoes knee surgery, there may be a painfulyet effective regimen of therapy that needs to be followed in order torehabilitate the knee. This method and apparatus could be applied to theknee rehabilitation therapy. Another therapy might be physical fitnessroutines to prevent heart disease or the like. Many fitness centers nowinclude machines that electronically log a person's activity on a weightmachine or a cardio machine. This data could be used to indicateadherence to a regimen. When physical activity drops off, messages couldbe sent to the patient to motivate the person and keep them going.

FIG. 9 is a process flow diagram illustrating a method in which, at 910,data characterizing an individual is received. Thereafter, at 920, oneor more variables are extracted from the data so that, at 930, using apredictive model populated with the extracted variables, a likelihood ofthe individual adhering to a treatment regimen can be determined. Thepredictive model is trained on historical treatment regimen adherencedata empirically derived from a plurality of subjects. Subsequently, at940, data characterizing the determined likelihood of adherence can bepromoted.

A variety of predictive models can be utilized with the subject matterdescribed herein. The predictive models used herein to generate thetreatment adherence score and/or the patient response score can bebased, for example, on a scorecard model developed using theModelBuilder™ software suite of Fair Isaac Corporation. In someimplementations, a divergence-based optimization algorithm can betrained using the medical/adherence data from a plurality of patients.The underlying predictive model can use a variety of predictivetechnologies, including, for example, neural networks, support vectormachines, and the like in order to adherence and/or response of a singleindividual based on historical data from a large number of subjects.

Various implementations of the subject matter of the method andapparatus described avoe may be realized in digital electroniccircuitry, integrated circuitry, specially designed ASICs (applicationspecific integrated circuits), computer hardware, firmware, software,and/or combinations thereof. These various implementations may includeimplementation in one or more computer programs that are executableand/or interpretable on a programmable system including at least oneprogrammable processor, which may be special or general purpose, coupledto receive data and instructions from, and to transmit data andinstructions to, a storage system, at least one input device, and atleast one output device.

These computer programs (also known as programs, software, softwareapplications or code) include machine instructions for a programmableprocessor, and may be implemented in a high-level procedural and/orobject-oriented programming language, and/or in assembly/machinelanguage. As used herein, the term “machine-readable medium” refers toany computer program product, apparatus and/or device (e.g., magneticdiscs, optical disks, memory, Programmable Logic Devices (PLDs)) used toprovide machine instructions and/or data to a programmable processor,including a machine-readable medium that receives machine instructionsas a machine-readable signal. The term “machine-readable signal” refersto any signal used to provide machine instructions and/or data to aprogrammable processor.

To provide for interaction with a user, the method and apparatusdescribed above may be implemented on a computer having a display device(e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor)for displaying information to the user and a keyboard and a pointingdevice (e.g., a mouse or a trackball) by which the user may provideinput to the computer. Other kinds of devices may be used to provide forinteraction with a user as well; for example, feedback provided to theuser may be any form of sensory feedback (e.g., visual feedback,auditory feedback, or tactile feedback); and input from the user may bereceived in any form, including acoustic, speech, or tactile input.

The methods and apparatus described and contemplated above may beimplemented in a computing system that includes a back-end component(e.g., as a data server), or that includes a middleware component (e.g.,an application server), or that includes a front-end component (e.g., aclient computer having a graphical user interface or a Web browserthrough which a user may interact with an implementation of the subjectmatter of Appendix A), or any combination of such back-end, middleware,or front-end components. The components of the system may beinterconnected by any form or medium of digital data communication(e.g., a communication network). Examples of communication networksinclude a local area network (“LAN”), a wide area network (“WAN”), andthe Internet.

The computing system may include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

Although a few variations have been described and illustrated in detailabove, other modifications are possible. For example, the logic flowdepicted in the accompanying figures and described herein do not requirethe particular order shown, or sequential order, to achieve desirableresults. Other variations may be within the scope of the followingclaims.

1. A computer-implemented method comprising: receiving data characterizing an individual; extracting one or more variables from the data; determining, using a predictive model populated with the extracted variables, a likelihood of the individual adhering to a treatment regimen, the predictive model being trained on historical treatment regimen adherence data empirically derived from a plurality of subjects; and promoting data characterizing the determined likelihood of adherence.
 2. A method as in claim 1, further comprising: generating a treatment score indicative of the likelihood of adherence to the treatment regimen; and promoting the treatment score.
 3. A method as in claim 2, further comprising; associating, the treatment score, with one of a plurality of messages, the messages correlating to disjoint ranges of the treatment score; and initiating transmission of the associated message to the individual.
 4. A method as in claim 3, further comprising: determining a delivery channel for the associated message; and wherein the message is transmitted via the delivery channel.
 5. A method as in claim 2, further comprising; associating, the treatment score, with one of a plurality of sets of messages, the messages correlating to disjoint ranges of the treatment score, each set of messages providing sequential guidance to the individual to increase a likelihood of the individual adhering to the medical treatment, each set of messages further having an associating timeline for delivery; and individually transmitting messages in the set of messages based on the timeline for delivery.
 6. A method as in claim 1, further comprising: receiving additional data characterizing the individual, at least a portion of the additional data being generated subsequent to initiation by the individual of the treatment regimen; extracting one or more variables from the additional data, at least one of the variables being affected by initiation of the treatment regimen; determining, using the predictive model populated with the extracted variables from the additional data, a likelihood of the individual continuing to adhere to the treatment regimen; and promoting data characterizing the determined likelihood of the continued adherence.
 7. A method as in claim 1, wherein promoting the data comprises storing the data.
 8. A method as in claim 1, wherein promoting the data comprises transmitting the data to a remote server.
 9. A method as in claim 1, wherein promoting the data comprises displaying a treatment score in a graphical user interface.
 10. A method as in claim 1, further comprising: determining a likelihood for a patient response to the treatment regimen.
 11. A method as in claim 10, wherein the determining a likelihood for a patient response comprises: extracting one or more patient response variables from the data; determining, using a second predictive model populated with the extracted patient response variables, a likelihood of the individual responding to the treatment regimen, the predictive model being trained on historical treatment regimen response data empirically derived from a plurality of subjects; and promoting data characterizing the determined likelihood of the individual responding to the treatment regimen.
 12. A method as in claim 11, further comprising: generating a patient response score indicative of the likelihood of the individual responding to the treatment regimen; and promoting the patient response score.
 13. A method as in claim 12, further comprising: determining, associating the treatment score and the patient response score with one of a plurality of sets of messages, each set of messages providing sequential guidance to the individual to increase a likelihood of the individual adhering and responding to the medical treatment, each set of messages further having an associating timeline for delivery; and individually transmitting messages in the set of messages based on the timeline for delivery.
 14. A computer-implemented method comprising: receiving data characterizing an individual, the data being received at a plurality of sequential stages while the individual is undergoing a treatment regimen; extracting, at each stage, one or more variables from the data; determining, at each stage, using a predictive model populated with the extracted variables, a likelihood of the individual adhering to a treatment regimen, the predictive model being trained on historical treatment regimen adherence data empirically derived from a plurality of subjects; and promoting data, at each stage, characterizing the determined likelihood of adherence.
 15. A computer-implemented method comprising: receiving data characterizing an individual; extracting one or more variables from the data; determining, using a predictive model populated with the extracted variables, a likelihood of the individual responding to a treatment regimen, the predictive model being trained on historical treatment regimen responsiveness data empirically derived from a plurality of subjects; and promoting data characterizing the determined likelihood of responsiveness. 